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Biogeosciences, 17, 381–404, 2020https://doi.org/10.5194/bg-17-381-2020© Author(s) 2020. This work is distributed underthe Creative Commons Attribution 4.0 License.

Trace element composition of size-fractionated suspendedparticulate matter samples from the Qatari Exclusive EconomicZone of the Arabian Gulf: the role of atmospheric dustOguz Yigiterhan1, Ebrahim Mohd Al-Ansari1, Alex Nelson2, Mohamed Alaa Abdel-Moati3, Jesse Turner2,Hamood Abdulla Alsaadi1, Barbara Paul2, Ibrahim Abdullatif Al-Maslamani4, Mehsin Abdulla Al-Ansi Al-Yafei5,and James W. Murray2

1Environmental Science Center, Qatar University, P.O. Box 2713, Doha, State of Qatar2School of Oceanography, University of Washington, Seattle, WA 98195-5351, USA3Environmental Assessment Department, Ministry of Municipality and Environment, P.O. Box 39320, Doha, State of Qatar4Office of Vice President for Research and Graduate Studies, Qatar University, P.O. Box 2713, Doha, State of Qatar5Department of Biological and Environmental Sciences, Qatar University, P.O. Box 2713, Doha, State of Qatar

Correspondence: Oguz Yigiterhan ([email protected])

Received: 12 May 2019 – Discussion started: 17 May 2019Revised: 15 November 2019 – Accepted: 2 December 2019 – Published: 27 January 2020

Abstract. We analyzed net-tow samples of natural assem-blages of plankton, and associated particulate matter, fromthe Exclusive Economic Zone (EEZ) of Qatar in the Ara-bian Gulf. Size-fractionated suspended particles were col-lected using net tows with mesh sizes of 50 and 200 µm toexamine the composition of small- and large-size planktonpopulations. Samples were collected in two different years(11 offshore sites in October 2012 and 6 nearshore sites inApril 2014) to examine temporal and spatial variabilities. Wecalculated the excess metal concentrations by correcting thebulk composition for inputs from atmospheric dust using alu-minum (Al) as a lithogenic tracer and the metal/Al ratios foraverage Qatari dust. Atmospheric dust in Qatar is depleted inAl and enriched in calcium (Ca), in the form of calcium car-bonate (CaCO3), relative to the global average Upper Con-tinental Crust (UCC). To evaluate the fate of this carbonatefraction when dust particles enter seawater, we leached a sub-set of dust samples using an acetic acid–hydroxylamine hy-drochloride (HAc–HyHCl) procedure that should solubilizeCaCO3 minerals and associated elements. As expected, wefound that Ca was removed in Qatari dust; however, the con-centrations (ppm) for most elements actually increased af-ter leaching because the reduction in sample mass resultingfrom the removal of CaCO3 by the leach was more impor-tant than the loss of metals solubilized by the leach. Because

surface seawater is supersaturated with respect to CaCO3 andacid-soluble Ca is abundant in the particulate matter, we onlyused unleached dust for the lithogenic correction. Statisti-cal analysis showed that for many elements the excess con-centrations were indistinguishable from zero. This suggestedthat the concentrations of these elements in net-tow planktonsamples were mostly of lithogenic (dust) origin. These ele-ments include Al, Fe, Cr, Co, Mn, Ni, Pb, and Li. For severalother elements (Cd, Cu, Mo, Zn, and Ca) the excess con-centrations present after lithogenic correction are most likelyof biogenic/anthropogenic origin. The excess concentrations,relative to average dust, for most elements (except Cd) de-creased with distance from the shore, which may be due todifferences in biology, currents, proximity to the coast, or in-terannual processes.

1 Introduction

There have been few trace metal analyses carried out incoastal environments and marginal seas such as the ArabianGulf (Bu-Olayan et al., 2001; Khudhair et al., 2015). Data forthe major and trace elemental composition of marine partic-ulate matter are essential for our understanding of biogeo-chemical cycling in the ocean. Identification of the biotic

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382 O. Yigiterhan et al.: Trace element composition of size-fractionated suspended particulate matter samples

fraction is made complicated by the heterogeneity of par-ticle assemblages. In most areas of the open ocean marineparticles are mostly composed of phytoplankton and zoo-plankton (e.g., Krishnaswami and Sarin, 1976; Collier andEdmond, 1984). Their composition is influenced by a vari-ety of processes that include active uptake by phytoplank-ton, grazing by zooplankton, adsorption–desorption, particleaggregation, and microbial remineralization (e.g., Turekian,1977; Bruland and Franks, 1983; Bruland et al., 1991; All-dredge and Jackson, 1995). However, in the open ocean(e.g., Dammshauser et al., 2013; Rauschenberg and Twining,2015; Ohnemus and Lam, 2015), and especially in marginalseas and coastal regions, lithogenic, abiotic particles are alsoimportant (Collier and Edmond, 1984; Twining et al., 2008;Iwamoto and Uematsu, 2014; Ho et al., 2007). These abi-otic particles can include lithogenic minerals that are inputfrom rivers, atmospheric dust, and sediment resuspension aswell as authigenic particles such as Fe and Mn oxyhydrox-ides. The relative contributions of biotic and abiotic particlescan differ significantly. In some cases, the trace element con-tents of abiotic material can dominate the total composition(e.g., Ho et al., 2007). Distinguishing biogenic versus abi-otic elemental composition is complicated. There have beenfew studies that tried to distinguish the relative contributionsof biotic and abiotic particles in marine particulate matter(Lam et al., 2015; Ohnemus and Lam, 2015; Ohnemus et al.,2017; Wen-Hsuan Liao et al., 2017). Biogenic and abioticparticles are difficult to separate physically using most bulksampling techniques. Characterizing the composition of bio-genic particles is difficult due to the difficulties associatedwith sampling techniques and analyses (Martin and Knauer,1973; Collier and Edmond, 1984; Ho et al., 2003; Twininget al., 2004; Rauschenberg and Twining, 2015). Bulk chem-ical analyses can be used to determine the relative contribu-tions when the compositions of the end-members are known.There can be significant differences in composition spatiallyand among species of phytoplankton, making regional in-ferences difficult to evaluate when considering only averagedata sets. The biogenic portion can be determined after cor-recting for the lithogenic portion of the total concentration,using metal to aluminum ratios (Me/Al) and assuming Al asthe lithogenic tracer (Bruland et al., 1991; Ho et al., 2007;Yigiterhan et al., 2011, 2018). Metal to phosphorus (Me/P)ratios have been used as a way to normalize comparison ofthe biogenic portion of data sets from studies from differ-ent locations (Yigiterhan et al., unpublished data, 2020) us-ing different sampling techniques (e.g., Knauer and Martin,1981; Kuss and Kremling, 1999). Ho et al. (2007) used acombined Me/Al and Me/P approach to suggest that mostof the trace metals associated with plankton, in samples fromthe coastal and open South China Sea, were associated withextracellular abiotic (inorganic) particles of atmospheric ori-gin.

Particulate matter in seawater contains an important reser-voir that can be solubilized and made biologically available.

Some researchers have developed selective chemical leachapproaches to identify labile trace metals. No chemical leachtechnique will be able to perfectly mimic natural processes,but some have attempted to preferentially extract trace metalsthat are associated with intracellular material and particlesadsorbed only to cells (e.g., Sañudo-Wilhelmy et al., 2001;Twining et al., 2011). The acetic acid–hydroxylamine (HAc–HyHCl) leach with a short heating step, developed by Bergeret al. (2008), was designed to identify the fraction of tracemetals associated with cellular material and labile metalsthat may be adsorbed or associated with authigenic Mn/Feoxyhydroxides, which might be available to plankton. Theelement stoichiometries of individual cells can be deter-mined using synchrotron-based X-ray fluorescence (SXRF),ensuring accurate particulate measurements to ascertain bi-otic origin (Twining et al., 2003, 2004). However, thoughsurprisingly inexpensive, SXRF is time-consuming and notalways accessible, resulting in limited sample throughput.All approaches to distinguish biotic and abiotic composi-tions have advantages and disadvantages. A systematic com-parison of different methods was conducted by Rauschen-berg and Twining (2015). They concluded that the Berger etal. (2008) leach was the best approach for quantifying bioticand associated labile particulate phases likely to be involvedin marine biogeochemical cycling.

The area of our study was the Qatari Exclusive Eco-nomic Zone (EEZ), which is located at the center of theArabian/Persian Gulf (hereafter “the Gulf”). This region isheavily impacted by atmospheric dust, massive construc-tion, sediment dredging, heavy industrialization, oil and gasexploitation, marine transportation, and desalination plants,which renders it an ideal location to examine the influenceof these coastal processes on particulate trace element com-positions (Barlett, 2004; Richer, 2009; Al-Ansari et al., 2015;Jish Prakash et al., 2015; Yigiterhan et al., 2018). As there areessentially no rivers, atmospheric dust deposition is the mainsource of lithogenic particles to the surface marine waters ofthe Gulf. We examined the composition of local atmosphericdust in Qatar (Yigiterhan et al., 2018) and compared it withaverage Upper Continental Crust (UCC; Rudnick and Gao,2003) and local surface terrestrial deposits (TSD, Yigiterhanet al., 2018) to determine the legitimacy of using each for alithogenic correction. The aeolian dust in Qatar is rich in Ca(as potentially soluble CaCO3 and CaSO4 minerals), and thiscomplicates the use of Al as a tracer for the lithogenic correc-tion. We studied the effect of distance from the shore on theelemental concentrations in small- and large-size fractions ofnet-tow particulate matter in coastal seawater samples, withthe goal of determining the influence of dust, plankton, andanthropogenic sources in the Qatar EEZ.

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O. Yigiterhan et al.: Trace element composition of size-fractionated suspended particulate matter samples 383

2 Methods

The technique used in this study involved analyzing the to-tal composition of bulk samples collected using net tows ofnatural plankton assemblages. A subset of previously col-lected dust samples was leached using a modified versionof the acetic acid–hydroxylamine hydrochloride procedureof Berger et al. (2008). The apparent excess concentrations(biogenic plus anthropogenic) in the net-tow samples weredetermined by correcting for the lithogenic portion of thetotal concentration, using aluminum as the lithogenic tracer(Bruland et al., 1991; Ho et al., 2007; Yigiterhan et al., 2011)and the average composition of Qatari dust. This procedureprovides an estimate of the non-lithogenic portion and in-cludes labile elements that may be part of and available toplankton.

2.1 Study region

The Arabian/Persian Gulf is a 1000 km long and 338 kmwide arm of the Indian Ocean (Arabian Sea), covering233 100 km2 in area. The basin is semi-enclosed and shallowwith an average depth of 36 m. It is one of the hottest, driest,most saline, and dustiest areas on earth. In most of the Gulfseawater salinities range from 37 to 41. The Gulf is charac-terized by reverse flow and estuarine circulation (in at the sur-face and out at depth) with no sill or saddle restriction at thesouthern entrance in the Strait of Hormuz (Reynolds, 1993;Swift and Bower, 2003). The first comprehensive descrip-tion of the region’s hydrographic properties, nutrients, andcarbonate chemistry was presented by Brewer and Dyrssen(1985). They reported the following.

1. Strong south-to-north salinity gradients exist in theGulf, with salinities greater than S = 40 to the north.

2. Low nutrient levels everywhere in the surface waters,except for the surface inflow from the Gulf of Omanthrough the Strait of Hormuz.

3. The ratio of carbon fixed as CaCO3 to organic carbon,fixed during biological production, was about 2.5 to 1.

While there is a paucity of published data on primary pro-ductivity and diversity of phytoplankton in the region, ex-isting information reveals north-to-south trends (Rao andAl-Yamani, 1998; Dorgham, 2013; Polikarpov et al., 2016)which are driven by particular environmental features (e.g.,eutrophication, dust input, and coastal (shallow) versus open(deep) water). Phytoplankton biomass (chlorophyll a con-centration), primary production, abundance, species diver-sity, and species groupings, together with water quality pa-rameters, were measured in the Qatari EEZ by Quigg etal. (2013). Of the 125 species identified, 70 % to 89 % werediatoms (mainly Chaetoceros), 8 % to 22 % were dinoflagel-lates, and the remaining 2 % to 6 % were cryptophytes. Nococcolithophorids were identified.

2.2 Sample collection

Particulate matter samples were obtained during three sepa-rate expeditions in the Exclusive Economic Zone (EEZ) ofQatar. The first expedition was conducted on the R/V Jananfrom 13 to 14 October 2012 and included 11 sampling sitesin the Qatar EEZ, located at the east of the Qatar Peninsula(Fig. 1). Sampling locations were selected to include samplesfrom nearshore sites (Khor Al-Odaid, Mesaieed, Dukhan,Umm Bab) to offshore sites (Shraawoo’s Island, Al-Edd Al-Sharqi (Ash Sharqi), Al-Edd Al-Gharbi, Halul Island, andHigh North), along with varying proximity to industrial andurban areas. Some sites, such as Al-Khor and Khor Al-Odaid,are adjacent to environmentally protected areas in Qatar,while others, such as Mesaieed, Ras Laffan, Dukhan, andDoha, are subject to more industrial influence (Richer, 2009)and urbanization. Doha Bay is a major source of particles tothe adjacent Gulf due to intense human activity in the capitalcity and associated suburbs. Dredging, effluent discharges,extensive construction activities, and heavy traffic are ma-jor sources of particulate loading to the bay (Yigiterhan etal., 2018; Alfoldy et al., 2019). The water maximum bottomdepth range for 2012 sampling stations varied between 12and 58 m depth.

The second expedition was conducted using speedboatson 1 to 2 April 2014. Sampling sites were located westof Dukhan, an oil-producing region on the western coast(Dukhan Bay, Fig. 2a), and in shallow waters adjacent to thecity of Doha on the eastern coast (Doha Bay, Fig. 2b). At eachlocation, size-fractionated, duplicate, net-tow samples werecollected at three stations with increasing distance from thecoast along a relatively straight bearing. Sites were selectedto investigate how industrialization and coastal processes in-fluence nearshore elemental concentrations on the westernand eastern sides of the Qatar Peninsula, where marine wa-ters exhibit different physical and chemical properties. Thesampling depths varied between 2 and 5 m in quite shallowbay areas.

Additional sets of samples were collected during a thirdcruise from offshore locations in October 2014. The sam-pling was conducted using R/V Janan on a linear transectat six stations from the exit of the Doha channel to the bor-der of the EEZ of Qatar. The data from these samples willbe used in a later publication (Yigiterhan et al., unpublisheddata, 2020).

2.3 Sampling

Samples were collected at all locations using custom-designed plankton net tows with mesh sizes of 50 µm (smallsize fraction) and 200 µm (large size fraction) to examinesize-fractionated particulate matter. All sample sets were col-lected using nets towed horizontally at 1 to 2 m depth at1.5 knot speed for 10 min (∼ 0.5 km distance). Both samplescertainly contained variable assemblages of phytoplankton,

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Figure 1. Plankton sampling locations (red dots) for the offshore sampling cruise in October 2012. The land-based sampling stations foratmospheric dust are shown with yellow dots. The Ocean Data View (ODV) program was used for plotting the figure (Schlitzer, 2018).

zooplankton, a small fraction of other planktonic organisms,and terrestrial particles of atmospheric origin (e.g., dust);however, no attempt was made to visually determine theirproportions. Metal-free plankton ring nets of 200 cm lengthand 50 cm diameter (aspect ratio 4 : 1) were used to collectthese samples. The particulate samples were rinsed into thecod ends with surface seawater and transferred immediately,and onboard and under cover, into new, acid-cleaned 500 mLborosilicate glass jars with Teflon lids (Environmental Sam-pling Supply; Cutter et al., 2010). Each jar was carefullysoaked for 2 d in 25 % HNO3 and for another 2 d in 10 %HCL, and then rinsed five times with deionized (DI) water.They were also rinsed three times with surface seawater fromthe same location before filling with samples from the codend. The glass jars were kept in a cooler under ice and trans-ferred to 4 ◦C refrigerators onboard the vessel to allow par-ticles to settle in the dark. In the laboratory, within 2 h ofcollection, the excess seawater was decanted and particulatesamples were re-filtered under vacuum onto 142 mm diam-eter, 1 µm Nuclepore membrane filters. Particles were gen-tly rinsed using double-distilled DI water to remove excesssea salt before transfer to acid-washed 50 mL polypropylene

conical tubes (FALCON, Corning brand). The rinsing stepwas kept short to minimize breakup of fragile plankton (Col-lier and Edmond, 1984). As a result, some sea salt still re-mained in the samples. The contribution of sea salt to dryweight and concentrations of Ca, Mg, K, and Sr was cor-rected using concentrations of Na.

Samples for atmospheric dust (n= 5) were collected fromvarious locations around Qatar (Fig. 1, yellow dots on land),using automated and hand-sampling methods. These samplesare a subset of the complete data set for Qatar dust anal-yses presented in Yigiterhan et al. (2018). Dust accumula-tion is usually extensive, so collecting dust is very straight-forward, but was conducted carefully to avoid contamina-tion. Most individual dust samples were collected over 2-to 4-week intervals. The sampling was done using passivedust traps (Siap+Micros). The particles were collected inan acid-cleaned glass bowl, which has automated protectionfrom the rain with a digitally controlled closing–opening sys-tem, activated with rain sensors, for both dry and wet de-position. These traps were regularly visited during the non-stormy autumn and winter seasons from September 2013 toFebruary 2014. Sufficient dust particle accumulation (1 to



Figure 2. Station locations for the detailed nearshore sampling inApril 2014 near Dukhan Bay and Doha Bay. The Ocean Data View(ODV) program was used for plotting the figures (Schlitzer, 2018).

10 g dry weight per container) was achieved after a samplingperiod of 1 month or more for some stations. When the tar-geted sample weight was achieved, samples were immedi-ately collected. Sample extraction from the traps was mostlycompleted over a 48 h period. The dust particles were trappedaway from wind and anthropogenic local contaminants. Thisset of samples was analyzed in total (unleached) and leachedforms.

2.4 Sample processing and analyses

The particulate samples, which were previously trans-ferred into conical tubes, were centrifuged for 15 min at∼ 3000 RPM. Excess water was decanted at the end of thecentrifuge cycle and the wet sample weight was recorded.Samples were then kept frozen in the same 50 mL centrifugetubes at−24 ◦C until analysis. The frozen samples were des-iccated in a freeze drier for 1 week to 10 d before grinding atroom temperature using well-cleaned agate mortar and pes-tle. Trace metal clean techniques were applied during crush-ing and gently grinding to make a homogenous mixture. Thepowdered samples were transferred to new, trace metal cer-

tified 50 mL polypropylene tubes (UC475-GN – Ultimate-Cup™) to be weighed again for dry weight. These sampleswere stored at room temperature in a desiccator until aciddigestion.

Total acid digestion was completed on a HotBlock(SC154-240, Environmental Express) in acid-cleaned PTFMTeflon™ tubes. A subset of dried, homogenized samples(250± 10 mg) was transferred into each tube, followed byaddition of reagent grade 1 mL HF (ARISTAR® ULTRA,48 %, ultrapure), 5 mL HNO3 (MERCK, 65 %, Ultrapur),and 2.5 mL HCl (Honeywell Fluka™, 37 %, TraceSELECT)to each sample. The temperature of the HotBlock was ad-justed carefully, and then tubes were placed on the Hot-Block in a fume hood with loose caps at 95 ◦C for at least1 h. Addition of trace metal grade acids (HCI and HNO3)was repeated until a clear digest solution was obtained. De-pending on the lipid content of the samples, 1 mL or moreof trace metal grade H2O2 oxidizer (Riedel-de Haën, 30 %,extra pure, stabilized) was added before further increasingtemperature gradually to 135 ◦C. HotBlock heating contin-ued until near dryness. The sides of the tubes were rinsedconsecutively with 1 mL HNO3 and 5–10 mL of double-distilled deionized (DDI) water. Digests were placed backonto the HotBlock in the fume hood at 155 ◦C, without us-ing caps, and boiled until clear. Solutions were transferredto new, 50 mL polypropylene tubes (UC475-GN – Ultimate-Cup™) before rinsing the Teflon™ tube and cap at least threetimes with DDI water. The final sample volume was made upto 50 mL (by volume) by adding more DDI. Samples werestored at room temperature until analysis by an InductivelyCoupled Plasma Optical Emission Spectrometer (ICP-OES;PerkinElmer – Optima 7300 DV).

A subset of dust samples was also leached with aceticacid (HAc) and hydroxylamine hydrochloride (HyHCl) witha short heating step to remove more labile particulate con-centrations. Dried samples were weighed and then treatedwith a modification of the leaching process described inBerger et al. (2008). Comparative analyses by Rauschenbergand Twining (2015) and Berger et al. (2008) showed thatthis technique most effectively digested the biogenic portionwhile leaving the more refractory elements intact. The leach-ing solution was composed of Ultra-Pure 25 % acetic acid(pH 2) with 0.02 M hydroxylamine hydrochloride as a re-ducing agent. The amount of 5 mL of leachate was added to300 mg of homogenized, desiccated sample in a Nalgene testtube. For some large mesh size samples, less than 300 mgwas available, in which case the total sample was used. Thetest tubes were placed in a beaker of water and raised to atemperature of 95 ◦C for 10 min, and then left at room tem-perature for another 2 h before centrifuging at 2500 rpm for5 min. After the leachate was removed from the sample anddiscarded, 5 mL of DDI water was added to the tube andshaken vigorously to ensure mixing. Tubes were centrifugedat 2500 rpm for 5 min, after which the water was removed.This rinsing step was performed three times and the leachate



was discarded. The leached and rinsed solid samples werefreeze-dried, reweighed, and then totally digested using theprocedure described above with the leachable fraction deter-mined by the difference. These samples were also analyzedby ICP-OES to determine the total elemental concentrations.

2.5 Precision and accuracy

Duplicate samples, spiked samples, and certified referencematerials (CRMs) supplied by the National Research Coun-cil of Canada (NRC) were digested and analyzed twice witheach batch of samples (typically 1 in every 10 samples). Forprecision, three samples were analyzed in duplicate. To en-sure accurate measurements, four CRMs were included inthe analyses, i.e., PACS-2, PACS-3, MESS-3, and DORM-4.In almost all cases, the average measured values were withinthe 95 % confidence limits of certified values, and thus theaccuracy determined from this approach was comparable toor better than the precision. The limit of detection was de-fined as 3 times the standard deviation of blank readings.

2.6 Sea salt (sodium) correction

The contribution of sea salt to the total mass and concentra-tions of elements was corrected using Na as a tracer for seasalt. The correction was done in Eq. (1) as follows:

Mecorrected =Metotal−Nasample× (Me/Na)seawater. (1)

We used the Me/Na ratios in seawater from Pilson (2013).However, interpreting the correction can be complicated.There is uncertainty in this correction because of our assump-tion that all Na is from sea salt. The atmospheric dust inthis study area is rich in carbonate material. Recent marinecarbonate deposits and skeletons have Na concentrations be-tween 2000 and 5000 ppm or between 0.2 % and 0.5 % (Landand Hoops, 1973; Milliman, 1974) and the average Na inour dust samples was 1.89 %. Thus, some of the Na in ourmarine particulate matter samples could be naturally in thesamples, in the form of limestone and dolomite that orig-inated from the aeolian dust (Behrooz et al., 2017). Othermajor sources of Na in detrital sedimentary rock types aredetrital Na-feldspar and clay minerals. For example, the av-erage Na in our un-sea salt corrected particulate samples was60 000 ppm (or 6 %). But, because the Na varies so muchfrom sample to sample, we decided to use the total Na in eachsample for the sea salt correction. If we overestimate the seasalt correction, our sample mass would be too low and themetal concentrations would be too high. However, the mag-nitude of this uncertainty is within the natural variability, soit can be ignored.

2.7 Lithogenic correction

The total digestion procedure dissolved all abiotic as well asbiogenic material in the samples. In order to determine the

biogenic portion of the metal concentrations, we used alu-minum (Al) as a lithogenic tracer and assumed that all Al inthe samples was of lithogenic origin. Aluminum is an effec-tive tracer for lithogenic or terrigenous inputs due to its highcrustal abundance and generally little or no biological uptake(Ho et al., 2007). Because river input is absent in the QatariEEZ, and the underlying sediments are mostly carbonates,we assume that scavenging and biological cycling do not ap-ply for our atmospheric dust and water column samples.

Because the lithogenic samples can consist of materialsfrom different origins, it is best to compare analytical datausing Me/Al ratios, rather than absolute metal concentra-tions to correct for dust contributions (Yigiterhan and Mur-ray, 2008). The Me/Al ratios were calculated for two pos-sible lithogenic materials by dividing the concentration ofthe desired metal by the Al concentration. For determin-ing lithogenic contributions to our samples, we utilized av-erage total (unleached) Qatari dust (Table 1 in Yigiterhanet al., 2018) and average leached Qatari dust (Table 1, thisstudy). We multiplied these ratios by the Al concentration ineach sample to give a value for the lithogenic contributionof each element. After subtracting this value from the totalconcentrations, the remaining concentration was assumed tobe the excess metal (Meexcess) of biogenic, authigenic, or an-thropogenic origin (Yigiterhan and Murray, 2008; Yigiterhanet al., 2011). Excess metal concentrations were defined inEq. (2) as follows:

Meexcess =Metotal−Altotal×Me/Aldust. (2)

In this study, we only used the unleached dust compositionfor the lithogenic correction in Tables 2 and 3.

3 Results

3.1 Dust samples

Five dust samples were included in this study. They wereanalyzed before and after leaching, using the Berger etal. (2008) leaching procedure. The elemental concentrationdata for the total (unleached) and leached dust samples arereported in Table 1. The goal of this table is to put somelimits on the composition of lithogenic particles, originatingfrom terrestrial rocks that are input to the Qatari EEZ fromthe atmosphere. The total and leached compositions are com-pared with the global average UCC from Rudnick and Gao(2003) as ratios of total/UCC (column 4) and leached/UCC(column 7). We show this comparison because the UCC issometimes used for the lithogenic composition when localdata sets are not available and we want to show how Qataridust differs from the UCC. Ratios greater than 1 indicatethat the concentrations of elements in these dust samplesfrom Qatar are greater than UCC values. These elements in-clude Ca, Cd, Li, Mg, Mo, Sr, and Zn. As reported previ-ously (Yigiterhan et al., 2018), Qatari dust samples had Ca



Table 1. Average composition of five representative samples of total (strong acid-digested, unleached) Qatari dust, comparison with theaverage Upper Continental Crust (UCC; Rudnick and Gao, 2003), and composition of leached (total minus labile) Qatari dust. The total andleached compositions are compared with each other and with the UCC as ratios. The unleached and leached compositions are also given asMe/Al ratios. Bold values: calcium (Ca) is the major element of the Qatari dust, which has the highest value in column 4, while having thelowest value in column 6.

Element Dust (UCC)R&G (Total)avg/ Dust (D. leached)avg/ (Leached)avg/ (Me/Al)total (Me/Al)leach (Me/Al)UCC(Total)avg (UCC)R&G (Leached)avg (D. total)avg (UCC)R&G

(ppm) (ppm) (ratio) (ppm) (ratio) (ratio) (ratio) (ratio) (ratio)

Al 21 000 81 500 0.26 43 000 2.05 0.53 1 1 1As 2.08 4.8 0.43 7.01 3.37 1.46 9.90× 10−5 1.63× 10−4 5.89× 10−5

Ba 154 624 0.25 467 3.03 0.75 7.33× 10−3 1.09× 10−2 7.66× 10−3

Be 0.402 2.1 0.19 0.715 1.78 0.34 1.91× 10−5 1.66× 10−5 2.58× 10−5

Ca 137 000 25 700 5.33 75 100 0.55 2.92 6.52× 100 1.75× 100 3.15× 10−1

Cd 0.174 0.09 1.93 0.236 1.36 2.62 8.29× 10−6 5.49× 10−6 1.10× 10−6

Co 5.42 17.3 0.31 9.91 1.83 0.57 2.58× 10−4 2.30× 10−4 2.12× 10−4

Cr 52.6 92 0.57 147 2.79 1.60 2.50× 10−3 3.42× 10−3 1.13× 10−3

Cu 24.7 28 0.88 48.4 1.96 1.73 1.18× 10−3 1.13× 10−3 3.44× 10−4

Fe 11 200 39 200 0.29 26 100 2.33 0.67 5.33× 10−1 6.07× 10−1 4.81× 10−1

K 4260 23 200 0.18 9010 2.12 0.39 2.03× 10−1 2.10× 10−1 2.85× 10−1

Li 38.3 21 1.82 21.4 0.56 1.02 1.82× 10−3 4.98× 10−4 2.58× 10−4

Mg 29 000 15 000 1.93 57 900 2.00 3.86 1.38× 100 1.35× 100 1.84× 10−1

Mn 213 775 0.27 290 1.36 0.37 1.01× 10−2 6.74× 10−3 9.51× 10−3

Mo 1.5 1.1 1.36 51.6 34.40 46.91 7.14× 10−5 1.20× 10−3 1.35× 10−5

Na 6550 24 300 0.27 6,370 0.97 0.26 3.12× 10−1 1.48× 10−1 2.98× 10−1

Ni 34.5 47 0.73 211 6.12 4.49 1.64× 10−3 4.91× 10−3 5.77× 10−4

P 310 655 0.47 389 1.25 0.59 1.48× 10−2 9.05× 10−3 8.04× 10−3

Pb 3.64 17 0.21 9.05 2.49 0.53 1.73× 10−4 2.10× 10−4 2.09× 10−4

Sr 775 320 2.42 1100 1.42 3.44 3.69× 10−2 2.56× 10−2 3.93× 10−3

V 39.2 97 0.40 77.3 1.97 0.80 1.87× 10−3 1.80× 10−3 1.19× 10−3

Zn 85.1 67 1.27 212 2.49 3.16 4.05× 10−3 4.93× 10−3 8.22× 10−4

Note: “D.” means “Dust”.

concentrations 5.3 times larger than in the UCC. Sr(2.4×) >

Mg(1.9×)≥ Cd(1.29×) > Li(1.8×) were also enriched rel-ative to the UCC. Ratios less than 1 suggest that Qatari dustis depleted relative to the UCC. The ratios of leached to totalcompositions are also included in Table 1 to show the im-pact of leaching (column 6). The elements removed by theleach have a ratio < 1 and include Ca and Li. All other ele-ments may also be removed but have higher concentrationsin the leached material because of the reduction of mass(loss of CaCO3). The total and leached values are also com-pared using their Me/Al ratios, which is one approach of-ten used for comparing samples with variable compositions(columns 8, 9, 10). Clearly, use of UCC concentrations forthe end-member composition of atmospheric dust would notbe appropriate for our study because Qatari dust has a largecarbonate mineral content reflecting the source regions ofQatar.

3.2 Particulate samples

The elemental compositions of the two size classes of partic-ulate net-tow samples are given in Table 2 (October 2012)and Table 3 (April 2014). The present data are valuableas they represent the first with concentrations of particulatetrace elements in the Qatari EEZ. The raw data (column R;

Tables 2, 3) for the sea salt corrected composition of the size-fractionated marine particulate matter samples from the 2012and 2014 cruises are presented by net-tow mesh size. We cal-culated the lithogenic contributions for each element (col-umn L; Tables 2, 3) using average total (unleached) Qataridust (from Table 1) and the resulting excess concentrationsfor each element (column E; Tables 2, 3). Al only has col-umn R as it was used to calculate the lithogenic fraction.Average values and standard deviations for each size frac-tion are shown in the last row of each section. The cumula-tive average and standard deviation are shown at the bottom.The data are grouped by filter size with the 50 µm samplesat the top and the 200 µm samples at the bottom. For someelements in some samples, the dust correction (L) was largerthan the original signal (R), and this resulted in negative val-ues for excess metal (E) concentrations (Tables 2, 3). As neg-ative values are impossible it means that, in some cases, thelithogenic correction done with total (unleached) Qatari dustdata overcorrected for the lithogenic input. We treated this asa random error and argued that it is best to focus on the rowswith the average and standard deviation for the cumulative(50+ 200 µm) data set for each element.

We also calculated the excess concentrations assumingthat the lithogenic fraction had the average values of leached



Table2.D

atafor

thetrace

elementcom

positionof

planktonfrom

the2012

cruisew

ithaverage

Qataridust,used

forthe

lithogeniccorrection.T

heupper

segmentshow

sresults

fora

small-size

fraction(phytoplankton),collected

with

a50

µmnettow

.The

lower

segmentshow

sdata

forthe

largesize

fraction(zooplankton),collected

with

a200

µmnettow

.The

“Cum

ulative”row

isan

averageof

bothof

thesesize

fractions.Forboth

sizeclasses

andthe

Cum

ulativedata

averagesand

standarddeviations

aregiven.T

hedistance

column

givesthe

shortestdistancefrom

landto

eachsam

plinglocation

(inkilom

eters).The

lettersR

,L

,andE

representtheraw

data,thelithogenic

correction(in

thiscase

with

averageQ

ataridust),and

theexcess

concentrations,respectively.Allconcentrations

areparts

permillion

(ppm),orµg

g−

1.Alum

inumonly

hasa

rawdata

column

becausethe

entireA

lineach

sample

was

assumed

tobe

oflithogenicorigin,m

eaningthatthe

lithogeniccorrection

would

bezero.T

henum

berinscientific

notationin

parenthesesadjacentto

theL

column

isa

numerical

representationof

the[M

e]/A

lratiofound

inQ

ataridust(Yigiterhan

etal.,2018)used

todeterm

inethe

lithogeniccorrection.C

ertainelem

entshave

excessvalues,w

hichare

negative,indicating

thattherew

asan

overcorrectionforthe

lithogenicportion

ofthesam

ple.

Bulk

plankton(50

µm)

Al

As

Ba

Cd

Co

Cr

Cu

Sample

no.L

ocationD

istance(km

)R

RL

ER

LE

RL

ER

LE

RL

ER

LE

1D

oha10.19

259231.4

0.2631.2

14.118.97

−4.92

0.520.02

0.501.18

0.670.51

6.316.49

−0.18

9.33.04

6.22

KhorA

l-Odaid

0.23994

13.20.10

13.15.7

7.27−

1.59

1.010.01

1.001.00

0.260.75

3.722.49

1.2310.4

1.179.2

3M

esaieed0.60

30138.6

0.308.3

17.922.05

−4.11

0.640.02

0.621.14

0.780.37

11.597.54

4.05183.7

3.54180.2

4Shraaw

oo59.60

194313.9

0.1913.7

14.714.22

0.481.11

0.021.09

0.960.50

0.465.21

4.860.35

82.32.28

80.15

Al-E

ddA

l-Gharbi

46.55480

6.60.05

6.553.4

3.5249.84

0.810.00

0.810.52

0.120.39

1.201.20

0.0010.8

0.5610.2

6A

shShargiO

ilfield78.90

18309.2

0.189.0

26.813.39

13.362.71

0.022.70

0.950.47

0.486.40

4.581.82

41.42.15

39.37

HalulIsland

84.051026

10.90.10

10.812.4

7.514.88

3.590.01

3.581.02

0.260.76

8.512.57

5.9413.9

1.2112.7

8H

ighN

orth74.55

118212.2

0.1212.1

6.68.65

−2.02

2.030.01

2.020.88

0.300.57

3.712.96

0.7511.2

1.399.8

9R

asL

affan11.40

80316.1

0.0816.1

5.55.88

−0.33

1.560.01

1.560.52

0.210.31

4.002.01

1.9956.9

0.9456.0

10D

ukhan0.02

10524

28.51.04

27.569.5

77.02−

7.49

0.360.09

0.273.00

2.710.29

24.8726.35

−1.49

11.912.36

−0.5

11U

mm

Bab

0.487227

19.30.72

18.642.5

52.89−

10.43

0.550.06

0.492.38

1.860.52

17.5918.10

−0.51

12.68.49

4.1

Average

287415.4

15.224.5

3.421.35

1.331.23

0.498.47

1.2740.4

37.02×

SD6304

16.115.7

43.033.28

2.062.09

1.530.31

14.134.28

106.7107.6

Zooplankton

(200µm

)R

RL

ER

LE

RL

ER

LE

RL

ER

LE

12D

oha10.19

3,25317.9

0.3217.6

16.923.80

−6.86

0.700.03

0.681.17

0.840.33

8.968.14

0.8212.9

3.829.1

13K

horAl-O

daid0.23

10128.4

0.108.3

6.17.40

−1.34

1.170.01

1.171.30

0.261.04

3.072.53

0.549.8

1.198.6

14M

esaieed0.60

27747.6

0.277.4

11.820.30

−8.54

0.630.02

0.601.93

0.721.21

11.876.95

4.9214.1

3.2610.8

15Shraaw

oo59.60

72210.7

0.0710.6

4.85.28

−0.52

0.780.01

0.780.58

0.190.39

1.931.81

0.126.8

0.855.9

16A

l-Edd

Al-G

harbi46.55

6419.2

0.069.2

114.24.69

109.531.00

0.010.99

0.680.17

0.512.40

1.610.79

35.50.75

34.817

Ash

ShargiOilfield

78.90560

7.80.06

7.715.6

4.0911.55

2.070.00

2.060.48

0.140.34

2.091.40

0.6948.9

0.6648.2

18H

alulIsland84.05

33512.1

0.0312.1

12.82.45

10.332.74

0.002.74

0.430.09

0.352.65

0.841.81

12.70.39

12.319

High

North

74.551019

14.50.10

14.46.7

7.46−

0.78

2.270.01

2.260.70

0.260.44

2.612.55

0.0613.1

1.2011.9

20R

asL

affan11.40

41819.4

0.0419.4

2.83.06

−0.28

1.770.00

1.760.39

0.110.28

3.291.05

2.2514.1

0.4913.6

21D

ukhan0.02

556019.5

0.5519.0

37.640.69

−3.12

0.590.05

0.542.28

1.430.84

14.3513.92

0.439.5

6.533.0

22U

mm

Bab

0.481865

9.50.18

9.317.5

13.653.83

0.430.02

0.421.39

0.480.91

4.624.67

−0.05

8.72.19

6.6

Average

165112.4

12.222.4

10.341.29

1.271.03

0.605.26

1.1316.9

15.02×

SD3243

9.39.2

63.866.96

1.581.60

1.280.66

8.772.90

26.127.6

Cum

ulative2262

13.913.7

23.46.88

1.321.30

1.130.55

6.861.20

28.726.0

2×

SD5050

13.212.9

53.152.08

1.801.82

1.390.52

11.943.58

79.579.9



Tabl

e2.

Con

tinue

d.

Bul

kpl

ankt

on(5

0µm

)Fe

Mn

Mo

Ni

PbV

Zn

Sam

ple

no.

Loc

atio

nD

ista

nce

(km

)R

LE

RL

ER

LE

RL

ER

LE

RL

ER

LE

1D

oha

10.1

913

5913

84−

24.4

26.3

26.3−

0.02

116.

860.

1811

6.67

5.99

4.26

1.73

0.70

0.45

0.25

4.59

4.84

−0.

2525

.510

.515

.02

Kho

rAl-

Oda

id0.

2379

353

026

2.7

52.2

10.1

42.1

2.59

0.07

2.52

2.94

1.63

1.31

0.34

0.17

0.17

26.7

41.

8624

.89

78.9

4.0

74.9

3M

esai

eed

0.60

2200

1608

592.

150

.130

.519

.59.

260.

219.

047.

714.

952.

763.

010.

522.

4918

.70

5.63

13.0

818

4.5

12.2

172.

34

Shra

awoo

59.6

010

2310

37−

14.0

35.5

19.7

15.8

17.5

80.

1417

.44

4.77

3.19

1.58

0.50

0.34

0.16

6.62

3.63

2.99

89.8

7.9

82.0

5A

l-E

ddA

l-G

harb

i46

.55

239

256

−17

.313

.24.

98.

30.

000.

03−

0.03

1.17

0.79

0.38

0.33

0.08

0.25

1.27

0.90

0.37

24.5

1.9

22.6

6A

shSh

argi

Oilfi

eld

78.9

097

797

70.

831

.718

.513

.10.

310.

130.

185.

273.

012.

260.

800.

320.

485.

273.

421.

8568

.77.

461

.37

Hal

ulIs

land

84.0

543

754

8−

110.

616

.010

.45.

60.

270.

070.

195.

201.

693.

510.

190.

180.

012.

051.

920.

1373

.74.

269

.68

Hig

hN

orth

74.5

552

663

1−

104.

921

.512

.09.

50.

000.

08−

0.08

4.26

1.94

2.32

0.31

0.20

0.11

1.97

2.21

−0.

2436

.64.

831

.89

Ras

Laf

fan

11.4

044

542

916

.612

.88.

14.

60.

340.

060.

283.

121.

321.

800.

800.

140.

662.

081.

500.

5898

.83.

395

.510

Duk

han

0.02

5493

5617

−12

3.2

132.

710

6.7

26.1

0.18

0.75

−0.

5721

.06

17.3

03.

773.

031.

821.

2027

.31

19.6

67.

6648

.542

.75.

811

Um

mB

ab0.

4836

2838

57−

229.

388

.673

.315

.30.

320.

51−

0.20

14.1

111

.88

2.23

2.12

1.25

0.87

19.2

813

.50

5.78

41.7

29.3

12.4

Ave

rage

1556

22.6

43.7

14.5

13.4

313

.22

6.87

2.15

1.10

0.60

10.5

45.

1770

.158

.52×

SD32

7045

0.0

74.1

23.4

69.5

069

.52

11.5

61.

932.

171.

4520

.67

15.5

291

.398

.1

Zoo

plan

kton

(200

µm)

RL

ER

LE

RL

ER

LE

RL

ER

LE

RL

E

12D

oha

10.1

917

7017

3634

.237

.233

.04.

266

.15

0.23

65.9

27.

305.

351.

961.

150.

560.

586.

506.

080.

4249

.213

.236

.013

Kho

rAl-

Oda

id0.

2381

754

027

7.5

75.3

10.3

65.0

2.98

0.07

2.90

3.15

1.66

1.49

0.18

0.18

0.01

10.4

71.

898.

5890

.14.

186

.014

Mes

aiee

d0.

6020

0114

8052

1.0

57.1

28.1

29.0

17.6

30.

2017

.44

7.57

4.56

3.01

2.03

0.48

1.55

9.69

5.18

4.51

74.6

11.2

63.3

15Sh

raaw

oo59

.60

370

385

−15

.727

.07.

319

.717

.74

0.05

17.6

91.

731.

190.

540.

160.

130.

042.

991.

351.

6442

.32.

939

.316

Al-

Edd

Al-

Gha

rbi

46.5

531

534

2−

26.9

20.3

6.5

13.8

0.00

0.05

−0.

051.

581.

050.

520.

560.

110.

451.

801.

200.

6046

.72.

644

.117

Ash

Shar

giO

ilfiel

d78

.90

262

299

−36

.815

.35.

79.

70.

000.

04−

0.04

1.55

0.92

0.63

0.00

0.10

−10

.00

1.88

1.04

0.84

61.7

2.3

59.5

18H

alul

Isla

nd84

.05

136

179

−42

.45.

73.

42.

30.

100.

020.

082.

030.

551.

480.

140.

060.

080.

910.

620.

2864

.91.

463

.619

Hig

hN

orth

74.5

548

454

4−

59.9

22.5

10.3

12.1

0.00

0.07

−0.

073.

901.

672.

230.

570.

180.

392.

171.

900.

2745

.94.

141

.820

Ras

Laf

fan

11.4

022

222

3−

1.3

7.7

4.2

3.5

0.98

0.03

0.95

2.80

0.69

2.11

0.00

0.07

−0.

071.

220.

780.

4480

.61.

778

.921

Duk

han

0.02

2923

2967

−44

.079

.256

.422

.90.

000.

40−

0.04

11.6

09.

142.

461.

580.

960.

6214

.67

10.3

84.

2967

.322

.544

.822

Um

mB

ab0.

4898

899

6−

7.3

82.5

18.9

63.6

0.00

0.13

−0.

133.

983.

070.

920.

890.

320.

575.

303.

481.

8241

.87.

634

.2

Ave

rage

935

54.4

39.1

22.3

9.60

9.48

4.29

1.58

0.66

0.37

5.24

2.15

60.5

53.8

2×SD

1823

361.

658

.644

.739

.99

39.9

26.

431.

701.

360.

969.

175.

2533

.235

.3

Cum

ulat

ive

1246

38.5

41.4

18.4

11.5

111

.35

5.58

1.86

0.88

0.49

7.89

3.66

65.3

56.1

2×SD

2661

399.

665

.335

.855

.47

55.4

49.

501.

871.

821.

2216

.52

11.7

267

.872

.1



Table3.D

atafor

thetrace

element

composition

ofplankton

fromthe

2014cruise.T

heupper

segment,data

forthe

small-size

fraction(phytoplankton),collected

with

a50

µmnet

tow,is

separatedfrom

thelow

ersegm

entwith

datafor

thelarge-size

fraction(zooplankton),collected

with

a200

µmnettow

.The

“Cum

ulative”row

isan

averageof

bothof

thesem

easurements.For

bothsize

classesand

theC

umulative

dataaverages

andstandard

deviationsare

given.The

distancecolum

nis

acalculation

ofthe

shortestdistancefrom

landto

eachsam

plinglocation

(inkilom

eters).The

lettersR

,L

,andE

representthevalues

forraw

data,thelithogenic

correction(using

averageQ

atardust),and

theexcess

concentrations,respectively.A

llconcentrations

areparts

perm

illion(ppm

),orµg

g−

1.Alum

inumonly

hasa

rawdata

column

becauseall

ofthe

aluminum

ineach

sample

was

assumed

tobe

oflithogenic

origin,meaning

thatthelithogenic

correctionw

ouldbe

zero.The

numberin

scientificnotation

inparentheses

adjacenttothe

(L)colum

nis

anum

ericalrepresentationofthe

[Me]/A

lratiofound

inaverage

Qataridust(Table

1and

Yigiterhan

etal.,2018)used

todeterm

inethe

lithogeniccorrection.C

ertainelem

entshave

excessvalues,w

hichare

negative,indicating

thattherew

asan

overcorrectionforthe

lithogenicportion

ofthesam

ple.

Bulk

plankton(50

µm)

Al

As

Ba

Cd

Co

Cr

Cu

Sample

Location

Distance

(km)

RR

LE

RL

ER

LE

RL

ER

LE

RL

E

23D

ukhan8.77

41298.40

0.417.99

21.930.2

−8.29

0.800.03

0.761.77

1.060.71

10.310.2

0.0836.0

4.8531.1

24D

ukhan6.78

40359.62

0.409.22

21.729.5

−7.88

0.790.03

0.761.68

1.040.63

8.810.1

−1.27

65.24.74

60.525

Dukhan

1.318407

14.450.83

13.6142.2

61.5−

19.32

0.190.07

0.122.62

2.170.45

16.721.1

−4.32

21.79.87

11.826

Doha

6.544401

3.870.44

3.4328.6

32.2−

3.61

0.260.04

0.221.78

1.130.65

15.011.0

3.94311.2

5.17306.0

27D

oha2.20

32662.01

0.321.68

12.523.9

−11

.370.09

0.030.06

1.410.84

0.578.9

8.20.69

44.83.84

41.028

Doha

0.0614

4934.54

1.443.11

69.5106.1

−36

.580.10

0.12−

0.02

3.863.74

0.1244.0

36.37.70

35.917.02

18.9

Average

64557.15

6.5132.7

−14

.510.37

0.322.19

0.5217.3

1.1485.8

78.22×

SD8675

9.169.14

41.024.04

0.670.70

1.830.43

27.08.38

222.7225.8

Zooplankton

(200µm

)

Sample

Location

Distance

(km)

RR

LE

RL

ER

LE

RL

ER

LE

RL

E

29D

ukhan8.77

20547.04

0.206.83

14.115.0

−0.90

0.770.02

0.752.45

0.531.92

−27

.25.1

−32

.3631.9

2.4129.5

30D

ukhan6.78

44485.83

0.445.39

78.432.6

45.830.29

0.040.25

1.751.15

0.6017.5

11.16.34

6.95.22

1.631

Dukhan

1.314190

5.170.41

4.7633.9

30.73.19

0.120.03

0.092.15

1.081.07

12.610.5

2.08124.8

4.92119.9

32D

oha6.54

37294.10

0.373.73

22.027.3

−5.28

0.620.03

0.591.79

0.960.82

12.79.3

3.39367.6

4.38363.3

33D

oha2.20

76342.45

0.761.69

40.955.9

−14

.970.37

0.060.30

4.251.97

2.2844.4

19.125.23

28.08.97

19.134

Doha

0.065506

4.860.55

4.3224.8

40.3−

15.49

0.330.05

0.284.34

1.422.92

23.713.8

9.8829.5

6.4723.0

Average

45944.91

4.4535.7

2.060.41

0.382.79

1.6018.5

2.4398.1

92.72×

SD3737

3.123.44

45.845.42

0.470.49

2.391.83

29.837.98

276.7277.9

Cum

ulative5524

6.035.48

34.2−

6.22

0.390.35

2.491.06

17.91.78

92.085.5

2×

SD6659

6.936.92

41.642.18

0.550.58

2.121.70

27.126.26

239.8241.9



Tabl

e3.

Con

tinue

d.

Bul

kpl

ankt

on(5

0µm

)Fe

Mn

Mo

Ni

PbV

Zn

Sam

ple

Loc

atio

nD

ist.

(km

)R

LE

RL

ER

LE

RL

ER

LE

RL

ER

LE

23D

ukha

n8.

7723

5822

0415

544

.141

.85

2.3

0.29

0.29

0.00

29.

76.

792.

91.

30.

720.

617

.07.

719.

311

616

.74

9924

Duk

han

6.78

2305

2111

194

43.1

40.9

02.

20.

140.

29−

15.0

09.

26.

632.

63.

40.

702.

716

.77.

549.

277

16.3

560

25D

ukha

n1.

3142

5844

86−

228

68.5

85.2

1−

16.8

0.10

0.59

−0.

4915

.513

.82

1.7

2.7

1.46

1.3

21.2

15.7

05.

530

34.0

7−

426

Doh

a6.

5426

0323

4925

540

.144

.60

−4.

50.

480.

310.

179.

77.

232.

40.

7611

.18.

222.

914

717

.84

129

27D

oha

2.20

1752

1743

923

.433

.11

−9.

7−

0.19

0.23

−0.

436.

55.

371.

12.

20.

571.

78.

36.

102.

245

13.2

432

28D

oha

0.06

7205

7735

−53

015

0.2

146.

903.

30.

401.

03−

0.63

19.8

23.8

2−

4.0

6.2

2.51

3.7

48.4

27.0

721

.367

58.7

59

Ave

rage

3414

−24

61.6

−3.

90.

20−

0.25

11.7

1.1

3.2

2.0

20.4

8.4

8054

2×SD

4084

604

91.5

16.2

0.49

0.62

9.8

5.2

3.7

2.4

28.9

14.0

8810

4

Zoo

plan

kton

(200

µm)

Sam

ple

Loc

atio

nD

ist.

(km

)R

LE

RL

ER

LE

RL

ER

LE

RL

ER

LE

29D

ukha

n8.

7717

5710

9666

145

.820

.82

25.0−

1.67

0.15

−1.

8220

.93.

3817

.51.

10.

360.

616

.83.

8413

.060

8.33

5230

Duk

han

6.78

3275

2374

902

83.4

45.0

838

.3−

1.78

0.32

−2.

1016

.97.

319.

6−

0.7

0.77

−1.

849

.98.

3141

.635

18.0

317

31D

ukha

n1.

3132

9922

3610

6370

.342

.46

27.8−

1.33

0.30

−1.

6314

.16.

897.

226

.70.

7325

.714

.97.

827.

114

016

.98

123

32D

oha

6.54

2874

1990

884

45.6

37.8

07.

8−

0.80

0.27

−1.

0617

.26.

1311

.10.

6513

.56.

966.

547

215

.12

457

33D

oha

2.20

5932

4074

1858

184.

477

.38

107.

0−

0.11

0.54

−0.

0724

.312

.55

11.7

128.

81.

3212

6.9

32.9

14.2

618

.613

430

.94

103

34D

oha

0.06

3434

2939

495

482.

555

.81

426.

60.

570.

390.

1814

.99.

055.

815

2.9

0.95

151.

626

.010

.28

15.7

380

22.3

235

7

Ave

rage

3429

977

152.

010

5.4

0.10

−1.

1818

.010

.561

.960

.625

.717

.120

418

52×

SD2,

743

951

339.

732

2.1

0.46

0.15

7.7

8.2

146.

714

6.2

28.0

25.8

359

358

Cum

ulat

ive

3421

452

106.

850

.80.

15−

0.72

14.9

5.8

32.5

31.3

23.1

12.7

142

120

2×SD

3317

1292

255.

324

5.6

0.47

1.56

10.7

11.8

115.

811

5.4

27.7

21.8

280

286



Qatari dust (from Table 1). The excess concentrations cal-culated that way were often negative because the “apparent”lithogenic correction was so much larger. As these values areunrealistic, those calculations are shown in Tables S1 (2012)and S2 (2014) in the Supplement.

The differences between the average excess metal concen-trations in small (50 µm) and large (200 µm) net-tow samplesare shown in Fig. 4. The average compositions of the twosize classes were in good agreement when you consider thestandard deviations. We ran t tests to determine whether theaverage small and large plankton samples were statisticallydifferent. For the 2012 data, there were only small statisti-cal differences for Ni and V. For the 2014 data, there weresmall statistical differences for Cr, Cu, Fe, and Ni. Thus, wecombined the data sets (50+200 µm) and reported the grandaverage and standard deviation for all samples as the cumu-lative rows at the bottom of Tables 2 and 3.

We conducted statistical tests to determine whether the cu-mulative means of the excess concentrations (E) in Tables 2and 3 were different from zero. If column E were not sta-tistically different from zero, it would support the argumentthat the elemental concentrations in the particulate sampleswere totally due to the presence of lithogenic material (Qataridust) and the contributions from plankton and anthropogenicsources were insignificant. The outcomes of the statisticalevaluations are shown in Table 4 (for 2012 data) and Table 5(for 2014 data).

All tests to determine whether E was different from zerowere one-sample tests using the combined plankton for agiven year. They were also one-sided with the alternativehypothesis that the sample was greater than zero. To decidewhich test to use, we first had to use the Shapiro test to es-tablish whether the sample concentrations were normally dis-tributed. If the Shapiro test is greater than 0.05, distributionsare normal. The outcomes regarding normality are listed inthe fifth column. Most distributions are not normal. If distri-butions are normal, you can use the t test. If not normal, youhave to use the Wilcoxon rank test. The sixth column showsthe test used. For the 2012 sample set, only Ni was normallydistributed (Table 4). For 2014 As, Cd, Fe, and Ni were nor-mal (Table 5).

We used the Wilcoxon rank test with alpha= 0.5 and theoutput is a p value for the difference of the median from zero.Those p values are shown in the third column. The answer tothe question “Is the value of column E different from zero?”is given in the second column (Tables 4, 5).

4 Discussion

Most marine particulate matter is composed of material ofbiotic origin with minor contributions from atmospheric dust(Ho et al., 2007; Rauschenberg and Twining, 2015). TheQatari EEZ is an especially unique site to study marine par-ticulate matter because of the large deposition rates of atmo-

spheric dust. After marine particulate matter is corrected forthe contribution of dust, we look for an answer to the ques-tion “Can plankton and anthropogenic signals still be identi-fied in the remaining concentrations?”.

4.1 Regional dust composition

The transport and deposition of aeolian dust are widely rec-ognized as an important physical and chemical concern toclimate, human health, and marine ecosystems (Rashki et al.,2013). Qatar is in a region heavily affected by dust stormsoriginating from a multitude of sources. Back trajectoriesshow that winds come from a variety of locations, but mostlyeither from the northwest (northern Saudi Arabia and Iraq) orthe southeast (Oman) (Yigiterhan et al., 2018). Because dustis potentially such an important source of lithogenic particlesto the surface waters of the central part of the Arabian Gulf,determining the composition of the total and potentially sol-uble dust end-member concentrations was an important partof this study. Ideally, with well-established end-members, wecould analyze the composition of the size-fractionated partic-ulate matter and determine the relative contributions of thedust and plankton end-members and possible anthropogeniccontributions. Fortunately, in this study, the composition oflocal dust in Qatar has been well characterized. This dust iscarbonate rich, which is different from the composition ofatmospheric dust from most other regions around the world,which is dominated by aluminosilicate material (e.g., Pros-pero et al., 1981; Mahowald et al., 2005; Lawrence and Neff,2009; Muhs et al., 2014; Patey et al., 2015).

The concentrations of all major and trace elements inQatari dust are different from those in the average UCC (Ta-ble 1). The most notable differences between the two arethe depletion of aluminum and the enrichment of calciumin Qatari dust. Yigiterhan et al. (2018) showed that the av-erage composition of Qatari dust has concentrations of Camuch higher than those predicted by the crustal Ca/Al ra-tios, suggesting that CaCO3(s), and possibly CaSO4(s) min-erals, are a major component of the total mass. In the UCC,aluminum has a higher concentration (8.2 %) than calcium(2.6 %). However, in unleached Qatari dust, the two ele-ments switch positions, with aluminum decreasing to 2.1 %by weight versus calcium, which is elevated to 13.7 % byweight (Table 1). Qatari dust has over 5.3 times the amountof calcium and about a quarter of the amount of aluminumcompared to the UCC. By converting Ca to CaCO3 and com-paring it with the total mass, we calculate that carbonateminerals make up 35 % or more of the total mass. The rea-son for this is due to the abundance of carbonate minerals inrock outcrops and soils in the Qatar Peninsula, thereby allow-ing carbonate minerals (calcite and dolomite) to be a majorcomponent of dust via erosional processes. The majority ofthe Qatar Peninsula is underlain by uniform limestone bedsof Miocene and Eocene age (Sadooni et al., 2014), and thesource areas to the north and south are similar. As a result,



Table 4. Are dust-corrected plankton concentrations statistically different from zero for 2012 data?

Element > Zero p_value Shapiro not_normal test_used t_test Wilcoxon

As Yes 0.000 0.007 non_normal Wilcoxon 0.000 0.000Ba No 0.538 0.000 non_normal Wilcoxon 0.114 0.538Cd Yes 0.000 0.013 non_normal Wilcoxon 0.000 0.000Co Yes 0.000 0.005 non_normal Wilcoxon 0.000 0.000Cr Yes 0.001 0.007 non_normal Wilcoxon 0.002 0.001Cu Yes 0.000 0.000 non_normal Wilcoxon 0.003 0.000Fe No 0.815 0.000 non_normal Wilcoxon 0.188 0.815Mn Yes 0.000 0.001 non_normal Wilcoxon 0.000 0.000Mo Yes 0.009 0.000 non_normal Wilcoxon 0.034 0.009Ni Yes 0.000 0.637 normal t test 0.000 0.000Pb Yes 0.001 0.000 non_normal Wilcoxon 0.469 0.001V Yes 0.000 0.000 non_normal Wilcoxon 0.004 0.000Zn Yes 0.000 0.019 non_normal Wilcoxon 0.000 0.000

Table 5. Are dust-corrected plankton concentrations statistically different from zero for 2014 data?

Element > zero p_value Shapiro not_normal test_used t_test Wilcoxon

As Yes 0.000 0.156 normal t test 0.000 –Ba No 0.979 0.019 non_normal Wilcoxon – 0.979Cd Yes 0.001 0.059 normal t test 0.001 –Co Yes 0.000 0.017 non_normal Wilcoxon – 0.000Cr No 0.102 0.022 non_normal Wilcoxon – 0.102Cu Yes 0.000 0.000 non_normal Wilcoxon – 0.000Fe Yes 0.013 0.923 normal t test 0.013 –Mn Yes 0.046 0.000 non_normal Wilcoxon – 0.046Mo No 0.995 0.000 non_normal Wilcoxon – 0.995Ni Yes 0.003 0.817 normal t test 0.003 –Pb Yes 0.012 0.000 non_normal Wilcoxon – 0.012V Yes 0.000 0.017 non_normal Wilcoxon – 0.000Zn Yes 0.000 0.004 non_normal Wilcoxon – 0.000

the composition of Qatari dust from the northern and south-ern regions is very similar for most elements (Yigiterhan etal., 2018).

The enrichment of calcium carbonate, along with otherpossible metal–CaCO3 associations, leads to uncertainty re-garding how to best use the dust samples for the lithogeniccorrection. Because calcium carbonate is more soluble thanaluminosilicates, samples leached with HAc–HyHCl may infact be a more accurate representation of the actual concen-tration to use for the lithogenic correction. The compositionsof Qatari dust before and after the leaching process are com-pared in Table 1. Because the emphasis of the study was onparticles, we focused on how the composition of the parti-cles changes rather than on how much of the different ele-ments was solubilized. Certain elements that are usually as-sociated with calcium carbonates (e.g., Mg, Sr, Li) are ele-vated relative to the UCC in dust samples prior to leaching.If elements associated with these carbonate minerals are sol-ubilized in seawater, our results show that their concentra-tions in the leached dust samples could be elevated relative

to aluminum, causing the Me/Al ratio to be elevated as well.You would normally expect that this leach would remove andlower the concentrations of reactive elements. However, allelements other than Ca and Li increased in concentration be-cause the mass removed by the leach was more importantthan the removal of the individual elements by the leach. Asa result of mass removal during the leach, the concentrationsof Mo (3350 %), Ni (511 %), As (234 %), Ba (204 %), Cr(179 %), Pb (148 %), Zn (149 %), Fe (132 %), K (111 %),and Al (105 %) in the leached solids more than doubled. Thedifference between total and leached Qatari dust samples ex-pressed as the percent difference from the total compositionis shown as a bar graph in Fig. 3.

4.2 Fate of dust in seawater

It is uncertain how the composition of Qatari dust mightbe modified when the particles enter surface seawater. Dothe particles keep the composition they had in the atmo-sphere or does the CaCO3 dissolve (completely or partially),



Figure 3. The difference between unleached and leached Qatari dust samples expressed as the percent difference from the unleached com-position. Molybdenum (Mo) is not shown because its difference (+3350 %) was off scale.

Figure 4. The difference between the excess metal concentrations in the small-size fraction (50 µm, phytoplankton) and large-size fraction(200 µm, zooplankton) data sets. These represent the average of the cumulative data in the two data sets for 2012 and 2014. The verticalaxis is logarithmic. The value for Ba in the phytoplankton data is not shown because it was negative due to overcorrection and could not beplotted on a logarithmic graph.

thus changing the Me/Al ratios of the residual material?Carbonate mineral dissolution would be driven by the satura-tion state in the water column. Very little is known about thecarbonate chemistry in the Arabian Gulf, though a limitedearly study in 1977 (Brewer and Dyrssen, 1985) suggestedthat the surface seawaters were saturated with respect to cal-cite and that CaCO3 is being formed. One possible benefit ofthis carbonate-rich dust might be to buffer future increases inocean acidification (Doney et al., 2009) in the Arabian Gulf.

As a result of this weak acid leach much of the carbon-ate mineral (and associated element) fraction in Qatari dustwas removed into leachate solution. Equating the conditionsof the Berger et al. (2008) leach with what happens to dustparticles upon deposition in seawater is highly problematic.However, the leached dust gives the most extreme outcomewhere most of the CaCO3(s) and associated elements weresolubilized and removed. The elements that have net removalby the leach have a ratio < 1 and include Ca and Li. The



concentrations of all other elements increased in the leachedmaterial because of the reduction of mass. The ratio for Nawas close to 1.0 (0.97), which suggests that our sample rinseprocedure after collection removed most of the sea salt con-tamination. The data in Table 1 show that after the leach Cadecreased by 45 % and Li by 44 %.

We have recently found high concentrations of particulateCa in a new set of particulate net-tow samples (Yigiterhan etal., unpublished data, 2020). Significant Ca (> 95 %) is re-moved from both size fractions by the HAc–HyHCl leach,suggesting that CaCO3(s) is present in those water columnparticulate matter samples. CaCO3-forming plankton are rarein the Arabian Gulf (Quigg et al., 2013; Polikarpov et al.,2016). Thus, these data suggest that CaCO3 in Qatari dustdoes not dissolve upon entering seawater after atmosphericdeposition. This justifies our use of the unleached dust con-centrations for the lithogenic correction.

4.3 How much of the trace element composition iscontrolled by dust?

Atmospheric dust makes a major contribution to particlesin the water column of the Arabian Gulf. After making thelithogenic correction, we found that some elements had noexcess concentrations that could be attributed to plankton oranthropogenic sources. For the year 2012, two elements (Baand Fe) were not statistically different from zero. This meansthat the input of Qatari dust can explain the Ba and Fe com-position in Qatari marine particulate matter. The rest weredifferent from zero and there is a statistically recognizablebiological/anthropogenic component. For the year 2014, Ba,Cr, and Mo were not statistically different from zero. Tradi-tional box plots showing these differences visually are shownin Fig. 5.

4.4 Metal to aluminum ratios

Another approach for examining the question regarding theimportance of input from Qatari dust and excess metal con-centrations is to use plots of metal versus aluminum con-centrations (Fig. 6). We argue that those elements that cor-relate strongly with Al and have the same metal to aluminum(Me/Al) ratio as dust may be controlled by dust input. TheMe/Al plots include a best fit linear regression (solid line)and lines representing the Me/Al in the average UCC (longdashes) (Rudnick and Gao, 2003) and average Qatari dust(short dashes) (Table 1).

The grouping of elements is pretty clear. The elementswith trends parallel to Qatari dust include Co, Cr, Fe, Mn,Ni, Pb, and Li. Vanadium increases with Al but at a higherrate. This may be due to scavenging of V from seawater, asproposed by Jeandel et al. (1987) for the Mediterranean. Bar-ium increases with Al but at a lower rate. Perhaps Ba can besolubilized out of the dust when it enters seawater. Molyb-denum mostly increases with Al in agreement with Qatari

Table 6. Percent of total concentration that is represented by E (thedifference between total minus dust contribution). The larger thecontribution from dust, the lower the percentage.

Element % E (2012) % E (2014)

Al 0 0As 98 91Ba 29 −28Cd 98 87Co 48 40Cr 17 11Cu 90 90Fe 3 9Mn 44 49Mo 99 −X

Ni 33 31Pb 55 98V 46 52Zn 85 82

dust with the exception of six samples, from the year 2012data set, at low Al that have large excess Mo. Arsenic and Cuare uniformly higher than expected for Qatari dust and Cdis higher, especially at low Al concentrations. It is temptingto speculate that these elements are higher due to biologicalenrichment. Metal to phosphorus (Me/P) ratios are requiredfor comparison with other studies of plankton composition.But in the absence of data for P, we cannot separate planktonfrom anthropogenic sources in this study.

4.5 Plankton and anthropogenic sources

In addition to Qatari dust, plankton and anthropogenicsources are likely contributors to particle chemistry. Forthose elements where the average and standard deviationsfor the cumulative average particulate samples are not sig-nificantly different from zero, this means we cannot identifythe plankton composition by difference. That was the casefor Ba and Fe in the 2012 samples and for Ba, Cr, and Mo inthe 2014 data set. This is an oligotrophic region with typicalchlorophyll concentrations of 0.3–1.5 µgL−1 (e.g., Al-Ansariet al., 2015). It appears that plankton are just not abundantenough relative to the input of dust to be detected by the un-certainties in this approach.

For each element, we calculated the percent of the totalconcentration that was represented by the difference of thetotal minus dust (E). The percentages are given in Table 6.Based on the combination of the statistical analysis and theplots of Me versus Al, we propose the following general-izations. These are proposed here as hypotheses that can betested with future data sets that include Ca and P analyses.Our tentative generalizations are that



Figure 5. Box plots for statistical texts. Traditional box plot where the whiskers extend to 1.5 times in the interquartile range and thenany points that fall outside of that are shown as individual dots on the graph. The dashed line at zero serves as an indicator of where thedistribution lies relative to zero.

1. some elements in net-tow plankton samples could bemostly of lithogenic (dust) origin (E < 50 % of the to-tal). These include Al, Fe, Cr, Co, Mn, Ni, Pb, and Li.

2. Some elements may be mostly of bio-genic/anthropogenic origin (E > 80 % of the total).

These include As, Cd, Cu, Mo, and Zn.

4.6 Regional trends

The excess metal concentrations are what originate fromplankton and anthropogenic sources. In order to examineregional variability, we plotted the excess metal concentra-tions from Tables 2 and 3 versus distance from the shore(Fig. 7). Best-fit linear regressions are included to repre-sent trendlines. Excess concentrations for most elements de-creased with distance from the shore (Fig. 7). Ten of the 13elements (As, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, V, and Zn) hadhigh concentrations in the 0–10 km range. Several elementsmay also show a decreasing trend with distance greater than

10 km (Co, Cu, Fe, Mn, Ni, Pb), but the statistics are poor.Cadmium was the only element for which there was a signif-icant increasing trend with distance, with an r2 value of 0.68.As there was no increase in chlorophyll in the 0–10 km rangeit is unlikely that the high excess metal concentrations weredue to biological processes. Future studies should examinethis more closely.

We hypothesize that abiological coastal processes are themost likely cause of these gradients, but the exact natureof the processes is unclear. As a country heavily affectedby rapid population growth and new construction, nearshoresampling sites could be influenced by pollutants from dis-posal from outfalls (surface and groundwater) or industrial-ization. Nearshore dredging and reclamation are also impor-tant processes used to recontour the coastline of Qatar and toobtain CaCO3 and silicate minerals for cement factories. It isunlikely that these high values are due to dust deposition asthe excess concentrations are calculated relative to averageQatari dust. This nearshore enrichment phenomenon empha-



Figure 6.



Figure 6. Metal/Al ratios for the 2012 and 2014 net-tow samples. The dashed lines represent the Me/Al in average Qatari dust (Yigiterhanet al., 2018) and Upper Continental Crust (Rudnick and Gao, 2003) values. The solid lines represent to best-fit lines for cumulative values ofplankton samples. All concentrations are given in ppm.



Figure 7.

sizes the need for more detailed sampling of plankton andanthropogenic sources in regions close to the shore.

5 Conclusions

Net-tow samples of natural assemblages of plankton fromthe Exclusive Economic Zone of Qatar in the Arabian Gulfwere analyzed for elemental composition. Samples were col-lected using net tows with mesh sizes of 50 and 200 µm to ex-amine size-fractionated plankton populations. Samples werecollected in two different years (2012 and 2014) to exam-

ine temporal variability. Atmospheric dust from Qatar is themain source of lithogenic particles to surface seawater. Qataridust is depleted in aluminum and enriched in calcium rel-ative to the global average Upper Continental Crust (UCC)values. The fate of the carbonate fraction when dust particlesenter seawater is uncertain; thus, we leached a subset of dustand plankton samples with an acetic acid–hydroxylamine hy-drochloride procedure to solubilize CaCO3 minerals and as-sociated elements. We found that a significant amount of theCa was solubilized but that the metal/Al ratios for many el-ements increased after leaching because the change in mass



Figure 7. The excess concentrations of each element in alphabetical order after lithogenic correction using average Qatari dust versus theshortest distance from the sampling site to land. The graphs show the combined data from four data sets: 50 and 200 µm mesh net tows forboth 2012 and 2014.



due to removal of CaCO3 was more important than the lossof metals solubilized by the leach itself. Because the surfaceseawater of the Arabian Gulf appears to be supersaturatedwith respect to CaCO3 and the concentrations of particulateCa are large, we assumed that the CaCO3 in dust does notdissolved on entering the seawater.

Excess elemental concentrations due to plankton and an-thropogenic inputs were obtained by correcting total con-centrations for input of atmospheric dust. Statistical analy-sis showed that for some elements the excess concentrationswere indistinguishable from zero. This included Ba and Fein 2012 and Ba, Cr, and Mo in 2014. This suggests that theconcentrations of these elements in particulate matter can beexplained as having only a dust origin. For a second approachwe examined how the Me/Al ratios compared with the ratiosin average Qatari dust. The elements with trends parallel toQatari dust included Co, Cr, Fe, Mn, Ni, Pb, and Li. Vana-dium increases with Al, but at a higher rate. We made ten-tative generalizations based on the statistical and geochemi-cal approaches, based on the Me/Al ratios. Analysis of thenet-tow plankton samples shows that there can be two majorsources for these elements.

– The first group could be mostly of lithogenic (dust) ori-gin, when excess (E) value is less than 50 % of the totalconcentration. These elements include Al, Fe, Cr, Co,Mn, Ni, Pb, and Li.

– The second group is of mostly biogenic or anthro-pogenic origin, when excess (E) value is greater than80 % of the total concentration. These elements includeAs, Cd, Cu, Mo, and Zn.

The excess concentrations relative to average Qatari dust formost elements were higher in the 0 to 10 km distance fromthe shore. Cadmium was the one exception and had a statis-tically significant increase with distance from the shore. Thisvariability could be due to differences in biology and/or non-biological (possibly anthropogenic) processes.

Data availability. The data set underlying this paper is available athttps://doi.org/10.17882/71591 (Yigiterhan et al., 2020).

Supplement. The supplement related to this article is available on-line at: https://doi.org/10.5194/bg-17-381-2020-supplement.

Author contributions. OY was the first and corresponding author,was NPRP Project PI, contributed to dust and plankton sampling (allcampaigns), did data processing for figures and tables, and made amajor contribution to the NPRP project and manuscript text writing(Material and methods; Results; Discussion), Tables 1, 2, and 3, andFigs. 1, 2, 6, and 7.

EMAA was NPRP Project PI, the sampling cruises’ Chief Sci-entist and organizer, and coordinator of the NPRP project sampling

campaigns, and provided major technical and logistic support forevery step of NPRP project.

AN did the 2014 plankton sampling, lab processing and acid di-gestion, and text writing (Introduction).

MAAM was an NPRP Project participant, contributed to themanuscript text editing, provided passive dust samplers, and ob-tained the required permissions for dust sampling.

JT was responsible for Tables 1 and S1 and S2 in the Supple-ment, for building Figs. 3 and 4, and for data processing for leachedconcentrations.

HAA was responsible for all sample digestion and ICP-OESanalysis for plankton and dust samples, as well as raw data pro-cessing, quality assurance, and quality control.

BP built Table 6 and Fig. 5, was a cruise participant during the2014 plankton sampling campaigns, did lab work for cruise prepa-ration and after cruise sample handling, filtration, and sample aciddigestion, and performed data processing for calculations of excessconcentrations. All leaching experiments were done by UW groupmembers under her leadership.

IAAM was NPRP Project Co-PI and provided plankton sam-pling, sample gear, the research cruise sampling strategy, and lo-gistic support, in addition to designing the experiments and con-tributing to plankton sampling.

MAAAAY was NPRP Project Lead-PI. MAAAAY took on therole of official leader of the project. He worked to get the requiredcoast guard permissions, provided speedboats, and joined samplingcampaigns, and helped in writing the project and some text in themanuscript.

JWM was NPRP Project Co Lead-PI and made a major contribu-tion to the manuscript text writing (all sections), editing, and build-ing of Tables 4 and 5. JWM also made a great contribution to thisresearch by being team leader of the UW research team.

Competing interests. The authors declare that they have no conflictof interest.

Disclaimer. The manuscript’s contents are solely the responsibilityof the authors and do not necessarily represent the official views ofthe Qatar National Research Fund.

Acknowledgements. The authors would like to thank the QatarNational Research Fund (QNRF) for funding and supportingthis research project. The dedicated effort made by the cap-tain and crew of Qatar University R/V Janan during sam-pling was greatly appreciated. The technical help and sup-port of Caesar Flonasca Sorino, Marwa Mustufa Al-Azhari,Azenith Castillo, Abdul Rahman Al-Obaidly, Reyniel M. Gasang,Faisal Muthar Al-Quaiti, Shafeeq Hamza, Cherriath A. Cheriath,and Mehmet Demirel from QU-ESC, during sampling, prepara-tion, pretreatment, and analysis, are acknowledged. Colton Miller(School of Environmental and Forest Sciences, University of Wash-ington) helped with the statistical analysis. We would like to thankJassem Abdulaziz Al-Thani for his valuable comments and ed-its. We highly appreciate Qatar University, Environmental SciencesCenter, Ministry of Municipality and Environment, and Universityof Washington-School of Oceanography administration and staff for


https://doi.org/10.17882/71591

https://doi.org/10.5194/bg-17-381-2020-supplement


supporting this project at various stages. This study was made pos-sible by a grant from the QNRF under the National Priorities Re-search Program.

Financial support. This research has been supported by the QatarNational Research Fund (grant no. NPRP 6-1457-1-272).

Review statement. This paper was edited by S. Wajih A. Naqvi andreviewed by two anonymous referees.

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